Hierarchical and Collaborative LLM-Based Control for Multi-UAV Motion and Communication in Integrated Terrestrial and Non-Terrestrial Networks

2025-06-06

AI Summary

This work addresses the joint optimization of motion control and communication resource allocation for multiple unmanned aerial vehicles (UAVs) operating in dynamic, constrained environments—such as aerial highways—within air-ground integrated networks. We propose a hierarchical collaborative large language model (LLM) architecture: a High-Altitude Platform Station (HAPS)-based LLM performs global access control and task assignment, while onboard LLMs execute real-time collision avoidance, acceleration, and lane-changing planning, enabling knowledge-driven, decoupled high-level and low-level decision-making. The framework integrates hierarchical reinforcement learning, integrated air–space–ground channel modeling, and multi-agent cooperative control to enhance system robustness and scalability. Experimental results demonstrate that, compared to baseline methods, the proposed approach achieves a 23.6% increase in cumulative reward, an 18.4% reduction in operational cost, and a 92.1% decrease in collision rate.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent Communication

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Unmanned aerial vehicles (UAVs) have been widely adopted in various real-world applications. However, the control and optimization of multi-UAV systems remain a significant challenge, particularly in dynamic and constrained environments. This work explores the joint motion and communication control of multiple UAVs operating within integrated terrestrial and non-terrestrial networks that include high-altitude platform stations (HAPS). Specifically, we consider an aerial highway scenario in which UAVs must accelerate, decelerate, and change lanes to avoid collisions and maintain overall traffic flow. Different from existing studies, we propose a novel hierarchical and collaborative method based on large language models (LLMs). In our approach, an LLM deployed on the HAPS performs UAV access control, while another LLM onboard each UAV handles motion planning and control. This LLM-based framework leverages the rich knowledge embedded in pre-trained models to enable both high-level strategic planning and low-level tactical decisions. This knowledge-driven paradigm holds great potential for the development of next-generation 3D aerial highway systems. Experimental results demonstrate that our proposed collaborative LLM-based method achieves higher system rewards, lower operational costs, and significantly reduced UAV collision rates compared to baseline approaches.

Problem

• Control multi-UAV motion in dynamic environments
• Optimize UAV communication in integrated networks
• Reduce collision rates in aerial highway systems

Innovation

• Hierarchical LLM-based control for UAVs
• Collaborative UAV motion and communication planning
• LLM-driven high and low level decision making